Machine Theory

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9781601985583Author Mauricio A. AlvarezFormat PaperbackPage Count 86Imprint now publishers IncPublisher now publishers IncWeight(grams) 135gDimensions(mm)...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9781601986986Author Fredrik LindstenFormat PaperbackPage Count 158Imprint now publishers IncPublisher now publishers IncWeight(grams) 232gDimensions(mm)...

Over the last decade, the long-running endeavour to automate high-level processes in machine learning (ML) has risen to mainstream prominence. Beyond this, an even loftier goal is the pursuit of autonomy, which describes the capability of the system to...

Regression analysis is a key area of interest in the field of data analysis and machine learning which is devoted to exploring the dependencies between variables, often using vectors. The emergence of high dimensional data in technologies such as...

Can machine learning deliver AI? Theoretical results, inspiration from the brain and cognition, as well as machine learning experiments suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in...

Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a...

The metric learning problem is concerned with learning a distance function tuned to a particular task, and has been shown to be useful when used in conjunction with nearest-neighbor methods and other techniques that rely on distances or similarities...

Automated theorem proving represents a significant and long-standing area of research in computer science, with numerous applications. A large proportion of the methods developed to date for the implementation of automated theorem provers (ATPs) have...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9783988313683Author Carl DennisFormat PaperbackPage Count 102Imprint Carl DennisPublisher Carl DennisWeight(grams) 145g

Variational autoencoders (VAEs) are powerful deep generative models widely used to represent high-dimensional complex data through a low-dimensional latent space learned in an unsupervised manner. In this monograph the authors introduce and discuss a...

This monograph reviews a class of univariate piecewise polynomial functions known as discrete splines, which share properties analogous to the better-known class of spline functions, but where continuity in derivatives is replaced by continuity in...

Many problems of recent interest in statistics and machine learning can be posed in the framework of convex optimization. Due to the explosion in size and complexity of modern datasets, it is increasingly important to be able to solve problems with a...

Online auctions are one of the most fundamental facets of the modern economy and power an industry generating hundreds of billions of dollars a year in revenue. Online auction theory has historically focused on the question of designing the best way to...

Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is an important challenge in artificial intelligence. Two key approaches to this problem are reinforcement learning (RL) and planning. This monograph surveys...

The authors of this monograph survey recent progress in using spectral methods including matrix and tensor decomposition techniques to learn many popular latent variable models. With careful implementation, tensor-based methods can run efficiently in...

In contemporary science and engineering applications, the volume of available data is growing at an enormous rate. Spectral methods have emerged as a simple yet surprisingly effective approach for extracting information from massive, noisy and incomplete...

Embeddings provide concrete numerical representations of otherwise abstract items, for use in downstream tasks. For example, a biologist might look for subfamilies of related cells by clustering embedding vectors associated with individual cells, while a...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9781922304094Author Edward MizeFormat PaperbackPage Count 102Imprint Venture InkPublisher Venture InkWeight(grams) 159gDimensions(mm) 229mm * 152mm * 6mm

Over the last decade, Approximate Message Passing (AMP) algorithms have become extremely popular in various structured high-dimensional statistical problems. Many of the original ideas of AMP were developed in the physics and engineering literature and...

Thompson sampling is an algorithm for online decision problems where actions are taken sequentially in a manner that must balance between exploiting what is known to maximize immediate performance and investing to accumulate new information that may...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9781601986269Author Sebastien BubeckFormat PaperbackPage Count 138Imprint now publishers IncPublisher now publishers IncWeight(grams) 205gDimensions(mm)...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9781601985460Author Shai Shalev-ShwartzFormat PaperbackPage Count 102Imprint now publishers IncPublisher now publishers IncWeight(grams) 156gDimensions(mm)...

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Deep RL...

Datasets are growing not just in size but in complexity, creating a demand for rich models and quantification of uncertainty. Bayesian methods are an excellent fit for this demand, but scaling Bayesian inference is a challenge. In response to this...

An Introduction to Conditional Random Fields provides a comprehensive tutorial aimed at application-oriented practitioners seeking to apply CRFs. The monograph does not assume previous knowledge of graphical modeling, and so is intended to be useful to...

As data is a predominant resource in applications, Riemannian geometry is a natural framework to model and unify complex nonlinear sources of data. However, the development of computational tools from the basic theory of Riemannian geometry is laborious...

Random matrix theory plays a central role in statistical physics, computational mathematics and engineering sciences, including data assimilation, signal processing, combinatorial optimization, compressed sensing, econometrics and mathematical finance,...

Among the data structures commonly used in machine learning, graphs are arguably one of the most general. Graphs allow the modelling of complex objects, each of which can be annotated by metadata. Nonetheless, seemingly simple questions, such as...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9781601983442Author Ulrike von LuxorFormat PaperbackPage Count 54Imprint now publishers IncPublisher now publishers IncWeight(grams) 92gDimensions(mm) 234mm...

Reinforcement learning (RL) is one of the foundational pillars of artificial intelligence and machine learning. An important consideration in any optimization or control problem is the notion of risk, but its incorporation into RL has been a fairly...

Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models. They were first dedicated to linear variable selection but numerous extensions have now emerged such as structured sparsity or kernel selection. It...

Randomized algorithms for very large matrix problems have received a great deal of attention in recent years. Much of this work was motivated by problems in large-scale data analysis, largely since matrices are popular structures with which to model data...

This book presents some new concentration inequalities for Feynman-Kac particle processes. It analyzes different types of stochastic particle models, including particle profile occupation measures, genealogical tree based evolution models, particle free...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9781601988300Author Silvia ChiappaFormat PaperbackPage Count 102Imprint now publishers IncPublisher now publishers IncWeight(grams) 156gDimensions(mm) 234mm...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9781680836325Author Christian A. NaessethFormat PaperbackPage Count 134Imprint now publishers IncPublisher now publishers IncWeight(grams) 200g

Learning Representation and Control in Markov Decision Processes describes methods for automatically compressing Markov decision processes (MDPs) by learning a low-dimensional linear approximation defined by an orthogonal set of basis functions. A unique...

This book constitutes the thoroughly refereed post-conference proceedings of the 12th International Conference on Membrane Computing, CMC 2011, held in Fontainebleau, France, in August 2011. The 19 revised selected papers presented were carefully...

The two-volume set LNCS 6852/6853 constitutes the refereed proceedings of the 17th International Euro-Par Conference held in Bordeaux, France, in August/September 2011. The 81 revised full papers presented were carefully reviewed and selected from 271...

This book and its sister volume collect refereed papers presented at the 7th Inter- tional Symposium on Neural Networks (ISNN 2010), held in Shanghai, China, June 6-9, 2010. Building on the success of the previous six successive ISNN symposiums, ISNN has...

This book constitutes the refereed proceedings of the sixth International Conference on Artificial Neural Networks - ICANN 96, held in Bochum, Germany in July 1996. The 145 papers included were carefully selected from numerous submissions on the basis of...